Impact of SVM multiclass decomposition rules for recognition of cancer in gastroenterology images

In this work we study the impact of a set of bag-of-features strategies for the recognition of cancer in gastroen-terology images. By using the SIFT descriptor, we analyzed the importance and performance impact of term weighting functions for the construction of visual vocabularies. Further analyzes were conducted in order to ascertain the robustness of multiclass decomposition rules for Support Vector Machines with different kernels. Our study was extended by tailoring a decomposition rule that explores prior knowledge according the four grades of the Singh taxonomy (SDR). We found that SDR coupled with a frequency term weight function attained the best overall results (80%) when trained with an intersection kernel. It also outperformed standard decomposition rules when using a χ² kernel and attained competitive performances with a linear kernel.